저가 적외선센서를 장착한 이동로봇에 적용 가능한 격자지도 작성 및 샘플기반 정보교합
Grid Map Building and Sample-based Data Association for Mobile Robot Equipped with Low-Cost IR Sensors
2
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot’s orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot’s pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.
Abstract
Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot’s orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot’s pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.
참고문헌 (15)
- J. S. Gutmann, "An experimental comparison of localization methods", pp.1998, Proc. of IEEE
- S. Thrun, "The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures", International Journal of Robotics Research, 25, pp.403~429 (2006)
- M. Montemerlo, "FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem", pp.593~598 (2002), Proc. of AAAI02
- S. Thrun, "Learning Metric-Topological Maps for Indoor Mobile Robot Navigation", Artificial Intelligence, 98, pp.21~71 (1998)
- T. B. Kwon, "Thinning-based Topological Exploration Using Position Probability of Topological Nodes", Advanced Robotics, 22, pp.339~359 (2008)
- H. Durrant-Whyte, "Simultaneous Localization and Mapping: Part I, the essential algorithms", IEEE Trans. on Robotics and Automation Magazine, 13, pp.99~110 (2006)
- Yong-Ju Lee, "SLAM of a Mobile Robot using Thinning-based Topological Information", International Journal of Control, Automation, and Systems, 5(5), pp.577~583 (2007), 제어·로봇·시스템학회
- L. Feng, "Globally consistent range scan alignment for environment mapping", Autonomous Robots, 4, pp.333~349 (1997)
- D. Musicki, "Integrated Probabilistic Data Association", IEEE Trans. On Automatic Control, 39, pp.1237~1241 (1994)
- R. Siegwart, "Introduction to Autonomous Mobile Robots" (2004), The MIT Press
- L. Feng, "Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans", Journal of Intelligent and Robotic Systems, 18, pp.249~275 (1997)
- "Where am I?, University of Michigan"[URL]
- A. Elfes, "Using Occupancy grids for mobile robot perception and navigation", IEEE Journal of Computer, 22, pp.46~57 (1989)
- S. Thrun, "Probabilistic Robotics" (2005), The MIT Press
- H. Choset, "Principles of Robot Motion: Theory, Algorithms, and Implementations" (2005), The MIT Press